A GCN takes as input an arbitrarily structured graph and executes a series of layers which exploit the graph's structure to calculate their output features.
Graph convolutional networks (GCNs) are becoming increasingly popular as they can process a wide variety of data formats that prior deep neural networks cannot easily support.
Compressing the communication is one way to mitigate the overhead by reducing the inter-node traffic volume; however, the existing compression techniques have critical limitations to be applied for NLP models with 3D parallelism in that 1) only the data parallelism traffic is targeted, and 2) the existing compression schemes already harm the model quality too much.
Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems.
Therefore, we use the data of individual stocks to train our neural networks to predict the future performance of individual stocks and use these predictions and the portfolio deposit file (PDF) to construct a portfolio of ETFs.
The finance industry has adopted machine learning (ML) as a form of quantitative research to support better investment decisions, yet there are several challenges often overlooked in practice.
To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quantized networks.
We find that this is often insufficient to capture the distribution of the original data, especially around the decision boundaries.
Ranked #1 on Data Free Quantization on CIFAR10
To handle the hard constraint problem of differentiable co-exploration, we propose ConCoDE, which searches for hard-constrained solutions without compromising the global design objectives.
To solve the two problems together, we initially propose an attention module for convolutional neural networks by developing an AW-convolution, where the shape of attention maps matches that of the weights rather than the activations.
Thus, a common approach is to compute a reconstructed training dataset before compression.
In this paper, we present GradPIM, a processing-in-memory architecture which accelerates parameter updates of deep neural networks training.
no code implementations • 25 Jan 2021 • Ze-Bin Wu, Daniel Putzky, Asish K. Kundu, Hui Li, Shize Yang, Zengyi Du, Sang Hyun Joo, Jinho Lee, Yimei Zhu, Gennady Logvenov, Bernhard Keimer, Kazuhiro Fujita, Tonica Valla, Ivan Bozovic, Ilya K. Drozdov
This indicates that the pseudogap and superconductivity are of different origins.
Superconductivity Materials Science
In this study, we train deep neural networks to classify composer on a symbolic domain.
In such circumstances, this work presents DANCE, a differentiable approach towards the co-exploration of the hardware accelerator and network architecture design.
Inspired by the shortcuts and fractal architectures, we propose two Shortcut-based Fractal Architectures (SoFAr) specifically designed for BCNNs: 1. residual connection-based fractal architectures for binary ResNet, and 2. dense connection-based fractal architectures for binary DenseNet.
Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest.
Our intuition is that, the more similarity exists between the unknown data samples and the part of known data that an autoencoder was trained with, the better chances there could be that this autoencoder makes use of its trained knowledge, reconstructing output samples closer to the originals.
Target encoding is an effective technique to deliver better performance for conventional machine learning methods, and recently, for deep neural networks as well.
Moreover, the results show that future stock prices can be predicted even if the training and testing procedures are done in different countries.
Thus, we propose a new and robust tracking method using a Fully Convolutional Network (FCN) to obtain an object probability map and Dynamic Programming (DP) to seek the globally optimal path through all frames of video.